Although the systematic evaluation of feature attribution methods is a fairly recent topic, a number of attempts have already been made to systematically and objectively evaluate the quality of explanations. An early, intuitive way of evaluating feature attributions was proposed by [1]}. In this approach, the top \(k\)  most important features are removed by replacing them with random noise. Consequently, the difference in output of the model is measured. If the most important features are truly important to the model, we expect a sharp drop in confidence for the predicted class.
